Abstract

Senescence is a highly regulated developmental process in plants in which nutrients areremobilised from organs which are no longer required or are stressed so that they may beused by organs which are only just developing. Whilst much is known about the causesof and the resulting outcomes of this process, very little is known about the geneticmachinery which link them. Some genes have been identified as having a regulatoryrole in the senescence process, but many of these have been determined by a forwardgenetics approach whereby mutants are randomly screened for phenotypical e↵ects. Amuch better approach, where possible, is the reverse genetics approach whereby mutantsare sought for testing as they are suspected to demonstrate a phenotypical e↵ect.It was the purpose of this study to find novel ways of identifying those geneswhich may be highly regulatory of the senescence process and to determine how they areable to lead from several di↵erent known causes of senescence through to the senescenceresponse itself. It was hypothesised that, using measurements of the expression levels ofa very large number of genes throughout the senescence process, theoretical models ofregulation between those genes could be determined and that these theoretical modelswould allow specific interactions to be identified and explained using biological validationtechniques.A large microarray experiment, performed prior to the start of this project,measured the expression of over 30,000 genes in the Arabidopsis thaliana genome overa period of 21 days during natural senescence. By cleaning this data and fitting anANOVA driven model to the resulting intensity measurements, it has been possibleto separate e↵ects leading to observed expression changes. The levels of each of thesee↵ects were tested by F-tests and this has allowed the identification of 8,878 genes whichare significantly di↵erentially expressed during senescence.By first using theoretical models to find genes amongst the set of 8,878 whichdemonstrate highly robust regulatory behaviour on other genes in the set, 118 genes wereable to be isolated for further study. A senescence phenotype screen was developed toassess reduced-expression mutants of many of those 118 genes and 8 were shown tohave a significantly altered timing of senescence when compared with wild-type plants.The surrounding networks of each of those 8 genes were formed by applying theoreticalregulatory network modelling in another novel manner similar to a Metropolis-Hastingsapproach which identified a set of 75 genes providing a testable regulatory networkmodel.The resulting network model has been tested biologically to establish the accuracyof the predictions. Whilst many of the predictions were not confirmed, a vastnetwork has been identified surrounding two of the highly regulatory genes indicating ajunction of two separate pathways leading to the senescence response and providing anetwork structure which could be used in another round of theory and validation. Additionally,these results introduce new interesting questions about how the senescencenetwork may have evolved to respond to so many inputs.